Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches
This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics—stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)—for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees....
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Forest Science and Technology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21580103.2025.2456295 |
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| author | Pablo Antúnez Christian Wehenkel Erickson Basave-Villalobos Celi Gloria Calixto-Valencia César Valenzuela-Encinas Faustino Ruiz-Aquino David Sarmiento-Bustos |
| author_facet | Pablo Antúnez Christian Wehenkel Erickson Basave-Villalobos Celi Gloria Calixto-Valencia César Valenzuela-Encinas Faustino Ruiz-Aquino David Sarmiento-Bustos |
| author_sort | Pablo Antúnez |
| collection | DOAJ |
| description | This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics—stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)—for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity. |
| format | Article |
| id | doaj-art-704db43af7644df49b6c8b5d76a3b779 |
| institution | Kabale University |
| issn | 2158-0103 2158-0715 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Forest Science and Technology |
| spelling | doaj-art-704db43af7644df49b6c8b5d76a3b7792025-01-30T14:46:13ZengTaylor & Francis GroupForest Science and Technology2158-01032158-07152025-01-0111310.1080/21580103.2025.2456295Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric ApproachesPablo Antúnez0Christian Wehenkel1Erickson Basave-Villalobos2Celi Gloria Calixto-Valencia3César Valenzuela-Encinas4Faustino Ruiz-Aquino5David Sarmiento-Bustos6División de Estudios de Posgrado-Instituto de Estudios Ambientales, Universidad de la Sierra Juárez. Av. Universidad S/N, Oaxaca, México.;Instituto de Silvicultura e Industria de la Madera, Universidad Juárez del Estado de Durango, Durango, MéxicoINIFAP, CIR Norte-Centro, Campo Experimental Valle del Guadiana, Durango, MéxicoINIFAP, CIR Pacífico-Sur, Campo Experimental Iguala, Iguala de la Independencia, Guerrero, México.;División de Estudios de Posgrado-Instituto de Estudios Ambientales, Universidad de la Sierra Juárez. Av. Universidad S/N, Oaxaca, México.;División de Estudios de Posgrado-Instituto de Estudios Ambientales, Universidad de la Sierra Juárez. Av. Universidad S/N, Oaxaca, México.;Instituto de Estudios Ambientales, Universidad de la Sierra Juárez. Av. Universidad S/N, Oaxaca, MéxicoThis study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics—stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)—for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity.https://www.tandfonline.com/doi/10.1080/21580103.2025.2456295Forest managementmachine learning algorithms in forestrypredicting forest biomassRandom Forest algorithmallometric modelingquantile regression in forest management |
| spellingShingle | Pablo Antúnez Christian Wehenkel Erickson Basave-Villalobos Celi Gloria Calixto-Valencia César Valenzuela-Encinas Faustino Ruiz-Aquino David Sarmiento-Bustos Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches Forest Science and Technology Forest management machine learning algorithms in forestry predicting forest biomass Random Forest algorithm allometric modeling quantile regression in forest management |
| title | Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches |
| title_full | Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches |
| title_fullStr | Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches |
| title_full_unstemmed | Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches |
| title_short | Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches |
| title_sort | predictive modeling of volume and biomass in pinus pseudostrobus using machine learning and allometric approaches |
| topic | Forest management machine learning algorithms in forestry predicting forest biomass Random Forest algorithm allometric modeling quantile regression in forest management |
| url | https://www.tandfonline.com/doi/10.1080/21580103.2025.2456295 |
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